Data-analysis-based validation of product review data and linking to supply chain record data

ABSTRACT

Data-analysis-based validation of product review data and linking to product record data are provided to facilitate forwarding product-related guidance. The processing includes one or more processors obtaining record data documenting, at least in part, a product&#39;s specific supply chain history, and receiving by the processor(s) review data for the product. Based on data analysis, the processor(s) authenticates the review data of the product to establish verified review data, and links the verified review data to the record data documenting, at least in part, the product&#39;s supply chain history. The processor(s) provides to a user product-related guidance based, at least in part, on the verified review data for the product that has been linked to the record data documenting, at least in part, the product&#39;s supply chain history.

BACKGROUND

In today's economic marketplace, many consumers depend on reviews fordetermining which products to purchase. There are certain disadvantagesto current review platforms, however, that limit their usefulness andaccuracy for this purpose. On many review platforms, it is not possiblefor a potential consumer to independently and transparently verify thatthe review was left by a validator who is a genuine purchaser and/oruser of the product reviewed.

Additionally, the potential consumer is unable today to independentlyand transparently determine that the product reviewed contains the samecomponents as the product being offered for sale and being consideredfor purchase. For instance, if an individual is considering buying aproduct model number ABC123, it is possible that certain componentswithin the product may have been changed out after a review for theproduct has been posted. The changed components may not be from the samemanufacturer, or be of the same quality, source, etc., which couldultimately impact the longevity, functionality, quality, and/or overallreviews for product model number ABC123.

SUMMARY

Certain shortcomings of the prior art are overcome and additionaladvantages are provided through the provision, in one or more aspects,of a computer-implemented method, which includes obtaining, by one ormore processors, record data documenting, at least in part, a product'ssupply chain history, and receiving, by the one or more processors,review data for the product. Based on data analysis, the one or moreprocessors authenticate the review data of the product to establishverified review data, and link the verified review data to the recorddata documenting, at least in part, the product's supply chain history.Further, the computer-implemented method includes providing to a user,by the one or more processors, product-related guidance based, at leastin part, on the verified review data for the product that has beenlinked to the record data documenting, at least in part, the product'ssupply chain history.

Systems and computer program products relating to one or more aspectsare also described and claimed herein. Further, services relating to oneor more aspects are also described and may be claimed herein.

Additional features and advantages are realized through the techniquesdescribed herein. Other embodiments and aspects of the invention aredescribed in detail herein and are considered a part of the claimedaspects.

BRIEF DESCRIPTION OF THE DRAWINGS

One or more aspects of the present invention are particularly pointedout and distinctly claimed as examples in the claims at the conclusionof the specification. The foregoing and other objects, features, andadvantages of the invention are apparent from the following detaileddescription taken in conjunction with the accompanying drawings inwhich:

FIG. 1 depicts one embodiment of one or more product supply chains, anddocumenting thereof as record data, such as a blockchain record, for usein accordance with one or more aspects of the present invention;

FIG. 2 depicts one embodiment of a system for computing a record, suchas a blockchain record, for use in accordance with one or more aspectsof the present invention;

FIG. 3 depicts a block diagram of one embodiment of a computing node, ordata processing system, to implement processing, in accordance with oneor more aspects of the present invention;

FIG. 4 depicts one embodiment of a workflow that illustrates certainaspects of some embodiments of the present invention;

FIG. 5 depicts one embodiment of a system, illustrating certain aspectsof an embodiment of the present invention;

FIG. 6 illustrates various aspects of some embodiments of the presentinvention;

FIG. 7 illustrates another embodiment of a product supply chain andcomputing a record, such as a blockchain record, for the product'ssupply chain history, for use in accordance with one or more aspects ofthe present invention;

FIGS. 8A-8C depict another embodiment of workflows that illustratecertain aspects of some embodiments of the present invention;

FIG. 9 depicts a further embodiment of a workflow that illustratesvarious aspects of some embodiments of the present invention;

FIG. 10 depicts an embodiment of a cloud computing environment which canfacilitate implementing, or be used in association with, certain aspectsof an embodiment of the present invention; and

FIG. 11 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

The accompanying figures, in which like reference numerals refer toidentical or functionally similar elements through the separate views,and which are incorporated in and form a part of this specification,further illustrate the present invention and, together with the detaileddescription of the invention, serve to explain aspects of the presentinvention. Note in this regard that descriptions of well-known systems,devices, processing techniques, etc., are omitted so as not tounnecessarily obscure the invention in detail. It should be understood,however, that the detailed description and the specific example(s),while indicating aspects of the invention, are given by way ofillustration only, and not limitation. Various substitutions,modifications, additions, and/or other arrangements, within the spiritor scope of the underlying inventive concepts, will be apparent to thoseskilled in the art from this disclosure. Note further that numerousinventive aspects and features are disclosed herein, and unlessinconsistent, each disclosed aspect or feature is combinable with anyother disclosed aspect or feature as desired for a particularapplication of the concepts disclosed herein.

Note also that illustrative embodiments are described below usingspecific code, designs, architectures, protocols, layouts, schematics,or tools only as examples, and not by way of limitation. Furthermore,the illustrative embodiments are described in certain instances usingparticular software, tools, or data processing environments only asexample for clarity of description. The illustrative embodiments can beused in conjunction with other comparable or similarly purposedstructures, systems, applications, or architectures. One or more aspectsof an illustrative embodiment can be implemented in hardware, software,or a combination thereof.

As understood by one skilled in the art, program code, as referred to inthis application, can include both software and hardware. For example,program code in certain embodiments of the present invention can includefixed function hardware, but other embodiments can utilize asoftware-based implementation of the functionality described. Certainembodiments combine both types of program code. One example of programcode, also referred to as one or more programs or program instructions,is depicted in FIG. 3 as computer-readable program instructions 334, aswell as application programs 330, record data control code 336, reviewdata authentication code 337, and recommendation engine code 338, one ormore of which can be stored in memory 306 of computer system 302.Further examples include programs 346 and computer-readable programinstruction(s) 348 in data storage device 344 of FIG. 3.

As noted, in today's economic marketplace, many consumers rely onreviews for determining which products to purchase. There are certaindisadvantages to current review platforms, however, that limit theirusefulness and accuracy for this purpose. For instance, on many reviewplatforms, it is not possible for a potential consumer to independentlyand transparently verify that the review was left by a validator who isa genuine purchaser and user of the product reviewed.

Additionally, it is not possible to independently and transparentlydetermine that the product reviewed contains the same components as theproduct being offered for sale and being considered for purchase. Forinstance, if an individual buys a product model number ABC123, certaincomponents within the product may have been changed out based onavailability from the supplier. If a component is unavailable, then themanufacturing process typically does not stop. Rather, the manufactureroften selects a secondary supplier to ensure production continues, andthe new supplier will provide the component needed to complete thebuild. However, that component may not be from the same manufacturer, orbe of the same quality, source, etc., which could ultimately impact thelongevity, functionality, quality, and overall reviews for product modelnumber ABC123. Currently, there is no way for the potential consumer tocompare any supply chain differences between the reviewed product modelnumber ABC123, and the currently available product number ABC123.Although both are the same type of product, and even have the same modelnumber, there may be differences in the respective source, manufacturingand/or distribution supply chains, which could affect longevity,functionality, quality, etc., of one product in comparison to the other.

In addition, with current review platforms, it can be difficult for anentity within the product supply chain ecosystem to identify and makedesirable changes to the product, or modifications to the supply chain,based on today's review feedback, where there may be multiple supplychain variants of the product being sold.

Disclosed herein are computer-implemented methods, systems and computerprogram products that include program code executing on one or moreprocessors which authenticates or validates third party review data andlinks the verified review data to the specific record data documentingthe supply chain of the product being reviewed. This augmented productcomponent tracking and correlation via a trusted third party validationsystem(s) facilitates providing recommendations and taking dynamicactions based on the verified review data and different products' supplychains. Embodiments disclosed herein advantageously enable consumers, tohave a much higher level of trust and confidence in the review data, aswell as a product's authenticity that is correlated to the review data.In addition, manufacturers benefit in one aspect from embodiments of thepresent invention by, for instance, preventing review data from olderproduct versions possibly having a negative influence on newer productversions, such as newer product versions designed to address a specificissue (e.g., model number XYZ1 versus model number XYZ2, or softwarerelease 123.1 versus software release 123.7).

Embodiments of the present invention result in a provenance knowledgebase, and analysis across the entire history graph reveals insight intoproduct components and manufacturing processes. The analysis is notlimited to a single product, and its component parts. Rather, an elementor component can exist in one product, and also in another product, withboth products potentially being otherwise unrelated. However, if asimilar usage profile exists (for instance, operation in certainclimates or under certain conditions), and there is a component-wisefailure in one product, then it is possible for a similar collapse ofthe component in the other product. Data analysis disclosed herein isexpandable to all parts and combination of parts across products toadvantageously provide product-related guidance to potential consumers,as well as to manufacturers, or other entities in the product supplychain ecosystem, to take action, such as making better selections and/orto better refine the product, the supply or manufacturing process forthe product, and/or its supply chain.

To summarize, embodiments of the present invention include acomputer-implemented method, system and computer program product fordata-analysis-based verification of product review data and linking tosupply chain record data, where program code executing on one or moreprocessors obtains record data documenting, at least in part, aproduct's supply chain history, and receives review data for theproduct, such as from a validator or owner of the product. Based on dataanalysis, the program code executing on one or more processorsauthenticates the review data for the product to establish verifiedreview data, and links the verified review data to the record datadocumenting, at least in part, the product's supply chain history.Embodiments of the present invention also include program code executingon one or more processors that provides to a user, such as a potentialconsumer, or a manufacturer associated with the supply chain,product-related guidance based, at least in part, on the verified reviewdata for the product that has been linked to the record datadocumenting, at least in part, the product's supply chain history. Inone or more implementations, the product-related guidance depends on theparticular user. For instance, where the user is a consumer, theproduct-related guidance can be, or include, the verified review data,and where the user is a supply chain entity, such as the manufacturer ofthe product, the product-related guidance can be, or include, one ormore recommendations related to the product, the manufacturing processfor the product, and/or the product's supply chain. In one or moreembodiments, the recommendations can be generated by a cognitiverecommendation engine which includes a machine learning agent, such asdescribed herein. Note also that in one or more embodiments, the recorddata is, or includes, a blockchain record that documents, at least inpart, the product's supply chain history.

In one or more embodiments of the present invention, program codeexecuting on one or more processors receives other record datadocumenting, at least in part, another product's supply chain history,where the product and the other product are a same type of product, andcompares the record data of the product and the other record data of theother product to confirm that the product and the other product containidentical components, and further provides to the user an indicationthat the product and the other product contain the identical components.

In one or more embodiments, program code executing on one or moreprocessors receives other record data documenting, at least in part,another product's supply chain history, compares the record data of theproduct and the other record data of the other product to identify oneor more supply chain differences between the product and the otherproduct, and provides the supply chain difference(s) to the user. Incertain embodiments of the present invention, the one or more supplychain differences between the product and the other product include oneor more component differences between the product and the other product,where the product and the other product each include multiplecomponents.

In one or more embodiments, the record data includes product componentdetails, and the program code executing on one or more processorsanalyzes the verified review data via natural language processing toidentify based thereon a component of the product relevant, at least inpart, to the verified review data, and provides an indication to theuser of the identified component of the product relevant to the verifiedreview data. In certain embodiments, the program code executing on oneor more processors receives other record data documenting, at least inpart, another product's supply chain history, where the product and theother product are different types of products, and compares the recorddata of the product and the other record data of the other product toconfirm that the identified component is common between the product andthe other product, and provides to the user an indication that theidentified component is common between the product and the otherproduct.

In one or more embodiments, the user is a manufacturer of the product,the verified review data is provided by an end-user of the product, andthe program code executing on the one or more processors parses theverified review data using natural language processing and generatebased thereon feedback guidance to the manufacturer specific to one ormore aspects of the product's supply history. In certain embodiments ofthe present invention, the feedback guidance includes one or morerecommendations to the manufacturer pertaining the product's supplychain history, where the one or more recommendations are based, at leastin part, on the verified review data for the product.

In one or more embodiments, the blockchain record includes ownershipdata for the product, and the program code executing on the one or moreprocessors authenticates the review data by comparing identity data of avalidator providing the review data to the ownership data for theproduct to authenticate the validator, and thereby verify the reviewdata.

Embodiments of the present invention are inextricably tied to computingand provide significantly more than existing approaches to providing andusing product review data. For instance, embodiments of the presentinvention provide program code executing on one or more processors toexploit the interconnectivity of various systems, as well as to utilizevarious computing-centric data analysis and handling techniques, inorder to authenticate review data of a product to establish verifiedreview data, and link the verified review data to obtained record datadocumenting, at least in part, the product's supply chain history, andprovide to a user product-related guidance based, at least in part, onthe verified review data for the product that has been linked to therecord data documenting, at least in part, the product's supply chainhistory. Both the interconnectivity of the devices and/or computingsystems utilized, and the computer-exclusive data processing techniquesutilized by the program code, enable various aspects of the presentinvention. Further, embodiments of the present invention providesignificantly more functionality than existing approaches to providingproduct review data to, for instance, potential consumers, as well asfeedback to product manufacturers.

In embodiments of the present invention, program code executing on oneor more processors provides significantly more functionality, includingbut not limited to: 1) program code that obtains record datadocumenting, at least in part, a product's supply chain history; 2)program code that receives review data for the product; 3) program codethat, based on data analysis, authenticates the review data of theproduct to establish verified review data; 4) program code that, basedon data analysis, links the verified review data to the record datadocumenting, at least in part, the product's supply chain history; and5) program code that provides to a user product-related guidance based,at least in part, on the verified review data for the product that hasbeen linked to the record data documenting, at least in part, theproduct's specific supply chain history.

As illustrated in FIG. 1, in the course of commerce, products (goods,materials, etc.) can pass from multiple source or supplier entities 101,to multiple manufacturing entities 102, to multiple distributionentities 103, to an end-user or consumer who purchased, for instance,the product through one of multiple retail entities 104. The specificseries of source, manufacturing, transportation, distribution and retailentities involved with the product is commonly referred to as the supplychain 100 of the product. As product can change hands multiple timeswithin its supply chain, and be incorporated into other products, etc.,techniques to verify the quality and genuineness of the product canusefully be maintained along the supply chain. For instance, a buyer ofa product along the supply chain can find it useful to verify propertiessuch as, for instance, content, sourcing, provenance, regulationadherence, etc.

Recent developments in supply chain management have led toimplementations of blockchain records 110 for supply chain integrityassurance. Blockchain is a distributed method of managing a singleimmutable ledger of verified transactions. A blockchain ledger(interchangeably referred to herein as a “blockchain” or “ledger”) isdecentralized, i.e., no single central authority is in control of theledger entries or updates, rather, a network of authorized members shareand verify the records, or blocks, that are to be added to the ledger.Once added, a block is immutable, i.e., cannot be changed or deleted,before a block is committed to the ledger, blockchain technology allowsone or more verifications to be computed and applied to the block, andonly upon a satisfactory number and/or types of verification can a blockbecome a part of the ledger.

A block is uniquely identifiable in the ledger by an identifierassociated with the block, where the block identifier is unique withinthe ledger. Unless a block is last in a branch in the ledger, each blockis connected to a previous block and a next block. Therefore, theblockchain allows verification of the history of a transaction recordedin the particular block by providing access to the previous and nextblocks as far as needed, up and down the ledger branches.

A variety of blockchain-based supply chain management solutions can beimplemented to provide integrity management, such as using tags to avoidcounterfeiting, utilizing sensed data and records of each point oftransaction, billing contracts for various components, stock-keepingunit (SKU) stamps, similar product identifiers, material analysis,and/or other data to record transactions at different points in thesupply chain, and their relevant sub-components.

FIG. 2 illustrates a blockchain computational system 200 with which oneor more embodiments of the invention can be implemented. As shown,system 200 includes one or more data or transaction sources 202operatively coupled to at least one of a plurality of distributed peercomputing nodes 204-1, 204-2, . . . , 204-6. System 200 can have more orless computing nodes than illustrated in FIG. 2. Each computing node insystem 200 can be a computing system or data processing systemconfigured to maintain a blockchain, which as noted, is acryptographically secured (via a cryptographic hash function) record orledger of data blocks that represent respective transactions within themonitored environment. A cryptographic hash function is a cryptographicfunction which takes an input (or “message”) and returns a fixed-sizealphanumeric string, which is called the hash value (sometimes called amessage digest, a digital fingerprint, a digest, or a checksum).

In FIG. 2, computing nodes 204-4, 204-5, and 204-6 are shown eachmaintaining the same blockchain (respectively illustrated as blockchains206-4, 206-5, and 206-6). Although not expressly shown, each computingnode in system 200 is configured to be able to maintain this sameblockchain. Each blockchain is a growing list of data records hardenedagainst tampering and revision (i.e., secure). Each block in theblockchain (illustratively referenced as block 208 in blockchain 206-4)holds batches of one or more individual transactions and the results ofany blockchain executables (e.g., computations that can be applied tothe transactions). Each block typically contains a timestamp andinformation linking it to a previous block. More particularly, eachsubsequent block in the blockchain (e.g., 206-4, 206-5, 206-6, etc.) isa data block that includes a given transaction and a hash value of theprevious block in the chain (i.e., the previous transaction). Thecurrent transaction and the hash value of the prior transactions canitself be hashed to generate a hash value. Thus, each data block in theblockchain represents a given set of transaction data plus a set of allprevious transaction data (e.g., as illustratively depicted as 210 inFIG. 2).

Assume a new set of transaction data (new transaction TX) is obtainedfrom one of the one or more data sources 202, and received by computingnode 1 (204-1). Computing node 1 (204-1) can provide the new transactionTX to all or a subset of computing nodes in system 200. In this case,transaction data TX is sent to computing node 2 (204-2), computing node4 (204-4), and computing node 5 (204-5).

Note that computing node 204-5 is marked with a star symbol to denote itas a leader in a consensus protocol. That is, the computing nodes in thesystem 200 each are configured to participate in a consensus protocol aspeers with one peer being designated as a leader. Any peer can assumethe role of leader for a given iteration of the consensus protocol. Ingeneral, the leader receives all transactions from the participatingpeers in the system and creates a new block for the new transaction. Thenew block is sent out by the leader node to one or more of the otherpeer computing nodes (e.g., 204-3 and 204-6 as illustrated in FIG. 2)which double check (validate) that the leader computed the new blockproperly (i.e., the validating nodes agree by consensus). If consensusis reached, then the computing nodes in system 200 add the new block tothe blockchain they currently maintain. As a result, after the newtransaction TX is processed by the system 200, each computing nodeshould now have a copy of the same updated blockchain stored in itsmemory. Then, when a new transaction comes into the system 200, theabove-described process of adding the transaction to the blockchain isrepeated.

It is to be understood that any single computing node may itself serveas the receiver, validator, and block generator for of new transactiondata set. However, in the context of a consensus protocol, the morenodes that validate the given transaction, the more trustworthy the datablock is considered.

It is to be further understood that the above description represents oneillustrative blockchain computation process and that embodiments of theinvention are not limited to the above or any particular blockchaincomputation implementation. As such, other appropriate cryptographicprocesses can be used to maintain and add to a secure chain of datablocks in accordance with embodiments of the invention. Further,although described herein with reference to a blockchain record orblockchain-backed supply-chain monitoring, the record data documenting,at least in part, the product's specific supply chain history can beassembled and protected using any desired transaction ledger technique.

Advantages of a blockchain computational system include, but are notlimited to: (1) the ability for independent nodes to converge on aconsensus of a latest version of a large data set (e.g., a ledger), evenwhen the nodes are run anonymously, have poor interconnectivity and mayhave operators who are dishonest or otherwise malicious; (2) the abilityfor any well-connected node to determine, with reasonable certainty,whether a transaction does or does not exists in the data set; (3) theability for any node that creates a transaction to, after a confirmationperiod, determine with a reasonable level of certainty whether thetransaction is valid, able to take place, and become final (i.e., thatno conflicting transactions were confirmed into the blockchain elsewherethat would invalidate the transaction); (4) a prohibitively high cost toattempt to rewrite or otherwise alter transaction history; and (5)automated conflict resolution that ensures that conflicting transactionsnever become part of the confirmed data set.

Illustrative embodiments adapt the blockchain computational system 200of FIG. 2 to monitor, manage and document data associated with aproduct's supply chain. More particularly, in non-limiting, illustrativeembodiments, blockchain technology is applied to track and append dataassociated with a product's manufacturing and distribution supply chainhistory as transactions in the blockchain in a secure manner.

Management of supply chain data is useful for chronicling the history ofa particular product and, in certain embodiments, the linking of anauthenticated validator's review of the product to the product'sspecific supply chain. As noted, each entry associated with thechronicled history of the product can be embodied as a “transaction” ofthe blockchain. Blockchain technology can thus be used to securelymaintain supply chain data as transactions (i.e., transaction data),which can be used as described herein to establish trust, accountabilityand transparency with regard to a product and its associated reviews.

Furthermore, as explained herein, illustrative embodiments provide ablockchain computational system for implementing the above and othermanagement features wherein each computing node comprises controllermodules for managing transaction data and blockchain computation. Moreparticularly, one or more computing nodes in the system can beconfigured to track and detect product data.

As such, product transactions associated with a given stakeholder(someone or something that is associated with the given environment) arecompiled into a chain of product transaction blocks. The chain can beconsidered a chronicle of the product's path through time. When atransaction is conducted, the corresponding product parameters are sentto one or more of the computing nodes in the system for validation. Theone or more computing nodes establish a validity of the transaction andgenerate a new block. Once the new block has been calculated, it can beappended to the product's blockchain.

Aspects of an embodiment, or one or more features thereof discussedherein, can be configured as a modification of, or an enhancement to, asupply chain management system, with companion program code executing,for instance, in the supply chain management system itself, or a dataprocessing system in communication with the supply chain managementsystem. Data, such as transactions, can be provided by a variety ofinputs, such as by sensors and other devices, that capture real-timedata points along the supply chain. For instance, Internet of Things(IoT) enabled devices can be employed in various manufacturing machines,material storage, transport devices or vehicles, or locations along thesupply chain, to sense a variety of parameters such as, motion or lackthereof, changes in weight or humidity, elapsed time during a motion orlack thereof, force applied, distance traveled, speed or velocity, andmany other data points of a manufacturing or transport process. Othertypes of embedded sensors can also be used to collect and transmitsupply-chain-related data.

Using a blockchain management system, an embodiment can construct ablockchain record from data point inputs obtained from any of thesources noted. The embodiment can identify a component or product at aparticular point in the supply chain to which the data point relates. Inone or more embodiments discussed herein, the record data, or blockchainrecord, can be processed using a cognitive engine, along with, forinstance, natural language processing of third party review data relatedto the product to provide a user of the system with product-relatedguidance to assist the user in taking an action related to the product.

FIG. 3 depicts one embodiment of a data processing environment, orcomputing node, in which one or more aspects of illustrative embodimentscan be implemented. FIG. 3 is only an example and is not intended toassert or imply any limitation with regard to the environments in whichdifferent aspects of embodiments can be implemented. A particularimplementation can have many modifications to the depicted environmentbased on the description provided herein.

With reference to FIG. 3, a block diagram of a data processing system isshown in which one or more aspects of the present invention can beimplemented. Data processing system 300 is an example of a computer,such as server, or other type of device, in which computer usableprogram code or instructions implementing one or more processes may belocated for the illustrative embodiments.

As shown in FIG. 3, data processing system 300 includes, for instance, acomputer system 302 shown, e.g., in the form of a general-purposecomputing device. Computer system 302 can include, but is not limitedto, one or more processors or processing units 304 (e.g., centralprocessing units (CPUs)), a memory 306 (referred to as main memory orstorage, as examples), and one or more input/output (I/O) interfaces308, coupled to one another via one or more buses and/or otherconnections 310.

Processor 304 includes a plurality of functional components used toexecute instructions. These functional components include, for instance,an instruction fetch component to fetch instructions to be executed; aninstruction decode unit to decode the fetched instructions and to obtainoperands of the decoded instructions; instruction execution componentsto execute the decoded instructions; a memory access component to accessmemory for instruction execution, if necessary; and a write backcomponent to provide the results of the executed instructions.

Bus 310 represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include the Industry StandardArchitecture (ISA), the Micro Channel Architecture (MCA), the EnhancedISA (EISA), the Video Electronics Standards Association (VESA) localbus, and the Peripheral Component Interconnect (PCI).

Memory 306 can include, for instance, a cache 320, such as a sharedcache, which may be coupled to local caches 322 of processors 304.Further, memory 306 can include one or more programs or applications330, an operating system 332, and one or more computer readable programinstructions 334, as well as record data control code 336, review dataauthentication code 337, and recommendation engine code 338,implementing one or more aspects disclosed herein. Additionally, oralternatively, computer readable program instructions 334 can beconfigured to carry out one or more other functions of certainembodiments of the invention.

Computer system 302 can also communicate via, e.g., I/O interfaces 308with one or more external devices 340, one or more network interfaces342, and/or one or more data storage devices 344. Example externaldevices include a user terminal, a tape drive, a pointing device, adisplay, etc. Network interface 342 enables computer system 302 tocommunicate with one or more networks, such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet), providing communication with other computing devices orsystems.

Data storage device 344 can store one or more programs 346, one or morecomputer readable program instructions 348, and/or data, etc. Thecomputer readable program instructions can be configured to carry outfunctions of one or more aspects of the present invention.

Computer system 302 can include and/or be coupled toremovable/non-removable, volatile/non-volatile computer system storagemedia. For example, it can include and/or be coupled to a non-removable,non-volatile magnetic media (typically called a “hard drive”), amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and/or an opticaldisk drive for reading from or writing to a removable, non-volatileoptical disk, such as a CD-ROM, DVD-ROM or other optical media. Itshould be understood that other hardware and/or software componentscould be used in conjunction with computer system 302. Examples,include, but are not limited to: microcode, device drivers, redundantprocessing units, external disk drive arrays, RAID systems, tape drives,and data archival storage systems, etc.

Computer system 302 can be operational with numerous other generalpurpose or special purpose computing system environments orconfigurations. Examples of well-known computing systems, environments,and/or configurations suitable for use with computer system 302 include,but are not limited to, personal computer (PC) systems, server computersystems, thin clients, thick clients, handheld or laptop devices,multiprocessor systems, microprocessor-based systems, set top boxes,programmable consumer electronics, network PCs, minicomputer systems,mainframe computer systems, and cloud computing environments thatinclude any of the above systems or devices, and the like.

Note again that the depicted example of FIG. 3 is not meant to implyarchitectural limitations. Further, as noted, data processing system 300of FIG. 3 could be, for instance, a server, workstation, tabletcomputer, laptop computer, or other computing device.

As discussed, as part of product manufacturing and distribution, theentities involved, including the manufacturer, can user or implement amanagement system which provides record data documenting, at least inpart, a product's supply chain history. In one embodiment, amanufacturer can implement a blockchain-enabled, dynamic database, thattracks all components and materials used in a finished product. As thefinal product is assembled, each component specification and materialsources are included as part of the record data for that product, suchas based on a serial number or other unique product identifier. When theproduct changes hands to the distributor, and to the final purchaser,the ownership record for the product can be updated accordingly,however, the underlying blockchain-based record data for the product isimmutable, and offers a transparent way for manufacturers, consumers,and even the original component company sourcing material or one or moresub-components to the product, to know with confidence which componentsare part of which end-user product.

Advantageously, when a consumer leaves a review (that is, providesreview data), as a third party validator of a product, the review datawill not be generic review data for a certain model of a product, butrather will be linked in accordance with the concepts disclosed hereindirectly to the actual blockchain record for that product that thereviewer purchased, which (in one embodiment) contains data on therespective components of the product and manufacturers thereof. Byproviding program code to validate the review data and link thevalidated review data to the actual record data documenting, at least inpart, the product supply chain history, a system user is able to discernwhich suppliers or vendors were involved in the supply and assemblyprocess, and/or other factors, such as quality characteristics of whereproduct materials were sourced, mined, molded, etc., that contributed tothe overall longevity or quality of the product, as documented by theverified review data. For instance, the manufacturer can determinewhether materials supplied by one entity enhance product longevity orquality over materials supplied by another entity using the validatedreview data as feedback. With this granular analysis, companies canreverse-engineer the client or customer review data feedback to makebetter purchasing and manufacturing decisions, to enable higher qualityproduct manufacturing, and thereby increase future customersatisfaction.

Advantageously, in one or more aspects, computer-implemented methods,system and computer program product are disclosed herein which providevalidation of third party review data, and linking of the validatedreview data to record data documenting, at least in part, a product'sactual supply chain, in order to enable iterative feedback into thesupply chain, the manufacturing and assembly processes, that can resultin variant categorization of completed products, that are ranked basedon this consumer feedback loop. Identification of inflection points viainspection of the loop is then possible in order to dynamically provideguidance or recommendations using, for instance, predictive modeling,that pertain to variations in the processes and components, to maximizeconsumer and manufacturer satisfaction. Based on the provided guidanceor recommendations, the system user can take action, or depending on therecommendation or guidance, the system itself can be configured to makea modification, such as a modification to the product's supply chain(e.g., to the product's manufacturing process).

By way of example, FIG. 4 illustrates one embodiment of a workflowimplementing certain aspects of some embodiments of the presentinvention. As illustrated, program code executing on one or moreprocessors obtains record data documenting, at least in part, aproduct's supply chain history 400. By way of example, the record datacan be a blockchain record, as discussed herein. Program code executingon the one or more processors also receives review data for the product410. The review data can be received from one or more validators of theproduct, such as one or more owners of the product. Based on dataanalysis, the program code executing on the one or more processorsauthenticates the review data of the product to obtain verified reviewdata 420. For instance, in one or more embodiments, the program codeauthenticates one or more validators providing the review data for theproduct. In another embodiment, a uniform identifier provided with theproduct can be used to link the review data to the record data for theproduct. Further, based on data analysis, program code links theverified review data to the record data documenting, at least in part,the product's supply chain history 430. By way of example, the linkingcan be via a uniform identifier in one implementation, or in anotherimplementation, the verified review data can become part of the recorddata, if desired. The program code provides to a user product-relatedguidance based, at least in part, on the verified review data for theproduct 440. For instance, the verified review data can be provided to apotential consumer as the product-related guidance, with or withoutinformation from the record data documenting, at least in part, theproduct's supply chain history. Alternatively, or additionally, theproduct-related guidance can be provided to one or more entities in themanufacturing supply chain ecosystem, including one or morerecommendations generated by a recommendation engine, using the verifiedreview data and the record data documenting, at least in part, theproduct's supply chain history.

FIG. 5 depicts an embodiment of a technical environment or system intowhich various aspects of some embodiments of the present invention canbe implemented. By way of example, various computing nodes or systemscan be provided and used, including computing nodes 510-1 . . . 510-N,computing nodes 520-1 . . . 520-N, and computing node(s) 530, such as acloud-hosting environment. In the embodiment illustrated, computingnode(s) 530 executes program code 532 implementing one or more aspectsof the present invention including, for instance, a review dataauthentication and linking module 534, and a recommendation engine 536,which in one or more embodiments, uses a machine learning agent 538 andone or more models 540. Note that computing node(s) 530 of system 500 isshown in one embodiment as one or more computing resources of acloud-hosting environment, by way of example only. Further, in one ormore embodiments, review data authentication and linking module 534 andrecommendation engine 536, including machine learning agent 538 andmodel(s) 540, can alternatively be implemented, in whole or in part, atone or more of computing nodes 510-1 . . . 510-N, and/or computing nodes520-1 . . . 520-N.

As illustrated in FIG. 5, one or more record data sources 502, computingnodes 510-1 . . . 510-N, computing nodes 520-1 . . . 520-N, andcomputing node(s) 530, operatively couple to and communication via oneor more networks 505. Network(s) 505 can be, for instance, atelecommunications network, a local-area network (LAN), a wide-areanetwork (WAN), such as the Internet, or a combination thereof, and caninclude wired, wireless, fiber-optic connections, etc. The network(s)505 can include one or more wired and/or wireless networks that arecapable of receiving and transmitting data, including transactions,record data, review data, and product-related guidance, such asdescribed herein.

System 500 illustrates, in part, one embodiment of a distributedcomputing platform on which a blockchain computational system (such assystem 200 of FIG. 2) can be implemented. As shown, the distributedcomputing platform can include one or more data sources 502 that areoperatively coupled to a plurality of computing nodes 510-1 . . . 510-Nacross one or more communications networks 505. By way of example only,each computing node 510-1 . . . 510-N can be configured to include atransaction data controller 511, a blockchain controller 512, and a datadatabase or blockchain database 513. In one embodiment, transaction datacontroller 511 manages transaction data including, but not limited to,receiving or otherwise obtaining transaction data (identification data,use data, etc.), and blockchain controller 512 manages blockchaincomputation including, but not limited to, accessing the transactiondata and generating and validating block, and adding the block to ablockchain, such as described above in connection with FIG. 2. Recorddatabase 513 maintains a copy of the current version of the blockchaindatabase, in one embodiment.

In one implementation, data representing ID, location, use, as well asother data, with respect to a given product, such as the product'ssupply chain, is considered transaction data. Further, othernon-limiting examples of transaction data include in-product sensor dataand/or transport information, if desired. One skilled in the art willrealize that other examples of transaction data related to a product,and in particular, related to the product's supply chain history, can betracked and managed as part of the blockchain. Such transaction data iswhat is provided to any given computing node 510-1 . . . 510-N from datasource(s) 502, or some other computing node, for use in computing ablockchain, for example, as described above in the context of FIG. 2.Transaction data controller 511 is configured to receive or otherwiseobtain the transaction data for each computing node, while blockchaincontroller 512 is configured to compute the blockchain for eachcomputing node. As discussed above in connection with FIG. 2, at least aportion of the computing nodes can be configured to participate inconsensus protocol as peers (i.e., validating peers or validatingnodes).

In one or more implementations, system 500 further includes one or morecomputing nodes 520-1 . . . 520-N, which can include a distributednetwork of one or more review or rating modules 521, which facilitatedocumenting review data 522 provided by one or more validators of aproduct. In one embodiment, each review module 521 can be located, forexample, within a computer resource receiving an authenticatedvalidator's review of a product, that is, receiving verified reviewdata. Further, each review module 521 can be configured to reflectratings, votes, comments, complaints, etc., regarding the product, whichas described herein, can be linked to the blockchain database.Accordingly, the blockchain can include, or have associated therewith,an auditable trail of reviews, or review data.

Note also that a unique identifier (UID) or token for a product can beused to form a decentralized Internet of Things (IoT) instrumentnetwork, where devices of the network are “smart devices” that areconnected to the blockchain through the corresponding UIDs or tokens.This can allow for enhanced supply chain tracking of products. Such anIoT instrument network can be used by the computing platform of system500 depicted in FIG. 5. For instance, the items (in this case, product)can be tracked via data forwarded through network(s) 505, whichoperatively couples the computing nodes that store the blockchain, aswell as the review data.

On top of providing trusted data, system 500 can provide for theimplementation of various intelligent services, such as provided byreview data authentication and linking module 534, and recommendationengine 536, which uses a machine learning agent 538 and one or moremodels 540. In the embodiment illustrated, program code 532 can beprovided to execute on computing node(s) 530 which, for illustrativepurposes only, is depicted as being separate from computing node(s)510-1 . . . 510-N, and computing 520-2 . . . 520-N. This is anon-limiting example of an implementation. In one or more otherimplementations, computing resources on which one or more aspects ofreview data authentication and linking module 534 and recommendationengine 536 are implemented can, at least in part, be located within oneor more of computing nodes 510-1 . . . 510-N and/or computing nodes520-1 . . . 520-N. Further, in one or more embodiments, review modules521 could be associated with computing nodes 510-1 . . . 510-N, ifdesired.

Briefly described, in one embodiment, computing nodes 510-1 . . . 510-N,520-1 . . . 520-N and 530 can each include one or more processors, forinstance, central processing units (CPUs). Also, the respectiveprocessor(s) can include functional components used in the integrationof program code, such as functional components to fetch program codefrom locations such as cache or main memory, decode program code, andexecute program code, access memory for instructions, and write resultsof the executed instructions or code. The processor(s) can also includea register(s) to be used by one or more of the functional components. Inone or more embodiments, the computing resource(s) can include memory,input/output, a network interface, and storage, which can include and/oraccess one or more other computing resources and/or databases, asrequired to implement the inventive aspects described herein. Thecomponents of the respective computing resource(s) can be coupled toeach other via one or more buses and/or other connections. Busconnections can be one or more of any of several types of busstructures, including a memory bus or a memory controller, a peripheralbus, an accelerated graphics port, and a processor or a local bus, usingany of a variety of architectures. By way of example, and notlimitation, such architectures can include the Industry StandardArchitecture (ISA), the Micro-Channel Architecture (MCA), the EnhancedISA (EISA), the Video Electronic Standard Association (VESA), local bus,and Peripheral Component Interconnect (PCI). Examples of a computingnode or computer system which can implement one or more aspectsdisclosed herein are described further herein with reference to FIGS. 3,10 & 11. Note also that, depending on the implementation, one or moreaspects of each computing node can be associated with, licensed by,subscribed to by, etc., a company or organization, such as a company ororganization manufacturing, providing, operating, etc., the product atissue.

As noted, program code 532 executing on computing node(s) 530 executesreview data authentication and linking module 534, and in oneembodiment, recommendation engine 536, which can include a machinelearning agent 538 and model(s) 540, to provide recommendations to, forinstance, a manufacturer or other supply chain entity based on dataanalysis of verified review data linked to the record data, such as theblockchain record. In one implementation, the review data authenticationand linking module includes program code which obtains record data, suchas a blockchain record, that documents, at least in part, a product'ssupply chain history, and receives review data, such as review data 522,for the product. Based on data analysis, review data authentication andlinking module 534 authenticates the review data of the product toestablished verified review data. For instance, in one embodiment, thereview data authentication and linking module 534 can authenticate avalidator, such as an owner, providing the review data, and/or canverify the review data using a unique identifier associated with theproduct and provided with the review data. Review data authenticationand linking module 534 further links the verified review data to therecord data, such as by including the verified review data within therecord data, or blockchain record, or maintaining the verified reviewdata separate from the record data, but linked thereto via, forinstance, a unique identifier. Further, the review data authenticationand linking module 534 can include program code to provide to a userproduct-related guidance based, at least in part, on verified reviewdata for the product that has been linked to the record data documentingthe product's supply chain history, as described herein. Further, theverified review data linked to the record data can be used byrecommendation engine 536 to provide feedback guidance to, for instance,a consumer and/or the product manufacturer specific to one or moreaspects of the product's supply chain history.

FIG. 6 is an example machine learning training system 600 that can beutilized to perform machine learning by the recommendation engine, suchas described herein. Training data 610 used to train the model inembodiments of the present invention can include a variety of types ofdata, such as data generated by the data sources and/or computing nodesof the system. Program code, in embodiments of the present invention,can perform machine learning analysis to generate data structures,including modules or algorithms used by the program code to perform oneor more aspects disclosed herein, including generating one or morerecommendations to a supply chain entity based on validated review data.Machine learning (ML) solves problems that cannot be solved by numericalmeans alone. In this ML-based example, program code extracts variousfeatures/attributes from training data 610, which can be stored inmemory or one or more databases 620. The extracted features 615 areutilized to develop a predictor function, h(x), also referred to as ahypothesis, which the program code utilizes as a machine learning model630. In identifying machine learning model 630, various techniques canbe used to select features (elements, patterns, attributes, etc.),including but not limited to, diffusion mapping, principle componentanalysis, recursive feature elimination (a brute force approach toselecting features), and/or a random forest, to select the attributesrelated to the verified review data, record data, and/or a particularrecommendation. Program code can utilize a machine learning algorithm640 to train machine learning model 630 (e.g., the algorithms utilizedby the program code), including providing weights for conclusions orrecommendations, so that the program code can train any predictor orperformance functions included in the machine learning model 640, suchas whether a particular component of a product relates to the verifiedreview data being analyzed. The conclusions can be evaluated by aquality metric 650. By selecting a diverse set of training data 610, theprogram code trains the machine learning model(s) 640 to identify andweight various attributes (e.g., features, patterns) that correlate toenhanced performance of the machine learning agent implemented by thecomputing node. The model(s) 540 (FIG. 5) used by recommendation engine536 can be self-learning, as program code updates the model based onadditional review data received, as well as additional record dataobtained. For instance, in some embodiments of the present invention,the program code executing on computing node(s) 530 can utilize existingmachine learning analysis tools or agents to create, and tune, eachrespective model, based, for instance, on the verified review data, aswell as the record data, for one or more products.

Some embodiments of the present invention can utilize IBM Watson® aslearning agent. IBM Watson® is a register trademark of InternationalBusiness Machines Corporation, Armonk, N.Y., USA. In embodiments of thepresent invention, the respective program code can interface with IBMWatson® application programing interfaces (APIs) to perform machinelearning analysis of obtained data. In some embodiments of the presentinvention, the respective program code can interface with theapplication programming interfaces (APIs) that are part of a knownmachine learning agent, such as the IBM Watson® application programminginterface (API), a product of International Business MachinesCorporation, to determine impacts of data on an operational model, andto update the respective model, accordingly.

In some embodiments of the present invention, certain of the APIs of theIBM Watson® API include a machine learning agent (e.g., learning agent)that includes one or more programs including, but not limited to,natural language classifiers, Retrieve-and-Rank (i.e., a serviceavailable through IBM Watson® developer cloud that can surface the mostrelevant information from document data), concepts/visualizationinsights, trade-off analytics, document conversion, natural languageprocessing, and/or relationship extraction. In an embodiment of thepresent invention, one or more programs can be provided to analyze dataobtained by the program code across various sources utilizing one ormore of, for instance, a natural language classifier, Retrieve-and-RankAPIs, and trade-off analytics APIs. In operation, the program code cancollect and save machine-learned data used by the machine-learningagent.

In some embodiments of the present invention, the program code utilizesa neural network to analyze collected data, such as verified reviewdata, relative to a product's supply chain history (i.e., the recorddata) to generate one or more models used by the recommendation engine.This learning is referred to as deep learning, which is a set oftechniques for learning in neural networks. Neural networks, includingmodular neural networks, are capable of pattern (e.g., state)recognition with speed, accuracy, and efficiency, in situations wheredata sets are multiple and expansive, including across a distributednetwork, including but not limited to, cloud computing systems. Modernneural networks are non-linear statistical data modeling tools. They areusually used to model complex relationships between inputs and outputs,or to identify patterns (e.g., states) in data (i.e., neural networksare non-linear statistical data modeling or decision making tools). Ingeneral, program code utilizing neural networks can model complexrelationships between inputs and outputs and identified patterns ofdata. Because of the speed and efficiency of neural networks, especiallywhen parsing multiple complex data sets, neural networks and deeplearning provide solutions to many problems in multi-source processing,which the programming code, in embodiments of the present invention, canaccomplish to facilitate providing, for instance, product-relatedguidance, such as one or more recommendations, to a consumer, and/or toone or more entities associated with a product's supply chain history.

FIG. 7 illustrates another embodiment of a product supply chain 700,with associated record data 730 and review data 721 to be linked andform the basis for product-related guidance, in accordance with one ormore aspects disclosed herein.

As illustrated in FIG. 7, a large number of entities and stages can bepart of a product's supply chain, including multiple options formaterial sourcing 701, multiple options for component fabrication indifferent manufacturing factories 702, multiple shipping methods 703,multiple warehousing 704 and supplier 705 options, as well as themanufacturer or assembly entity 706, which provides to a dealer orretailer one or more variations of the product 707. A consumer 720 ofone of those variations 702 can provide review data 721 which is linked,as described herein, to the particular record data 710 (e.g., orblockchain record) established for the product's supply chain. Based onthe linked review data 721 and record data 710, the system can provideproduct-related guidance 730, such as one or more recommendations, toanother potential consumer and/or to one or more entities in the supplychain ecosystem for the product.

As illustrated in FIG. 7, in one implementation, the blockchain record710 can include transaction data that is both business-to-business (B₂B)data for the product, as well as business-to-consumer (B₂C) data, suchas ownership information and/or verified review data. As illustrated inFIG. 7, the final product might be product XYZ in marketing andpackaging, but due to supplier, vendor and process differences, thereare actually multiple variants in the product model XYZ when analyzeddown to the granular components and processes throughout the supplychain ecosystem. As illustrated, there are thus actually differentvariants of the product model. Advantageously, the data-analysis-basedvalidation of product review data and linking to the actual supply chainrecord data for the product, such as disclosed herein, accounts forthese variants in providing information and making recommendations to auser of the system that is trusted and specific to the product at issue.

FIGS. 8A-8C depict another embodiment of workflows that illustrate someaspects of some embodiments of the present invention.

As illustrated in FIG. 8A, in one or more embodiments, program codetracks, via a blockchain database, material supplies from source to thepoint of manufacture(s) 800. For instance, raw material supplies can betracked via the blockchain database from the original source to thepoint of manufacture(s) with unique identifiers (UIDs) for: L[ ]:location, Timestamp[ ]: date sourced, and related metadata.

Further, program code can track via blockchain database components fromcomponent manufacture(s) to product manufacture(s) 805. For instance, asmaterials are used in manufactured components, the database record foreach component can reference the particular raw material sources forthat component. As components are used in the manufactured finalproduct, the database record for each final product can be updated toreference each particular maker of each component, as well as to allowdata access down to the raw materials used, as mentioned above. By wayof example only, program code to construct a block for the blockchainwith this information as part of assembling the record data might be:

const block={

-   -   ‘index’: 1,    -   ‘timestamp’ 1506057125.900785,    -   ‘transactions’: [        -   {            -   ‘sender’: “8527147fe1f5426f9dd545de4b27ee00”,            -   ‘recipient’: “a77f5cdfa2934df3954a5c7c7da5df1f”,            -   ‘amount’: 5,            -   ‘component’: C            -   ‘product feature’: Pf            -   ‘review’: NLP/Gaussian O/P        -   }    -   ],    -   ‘proof’: 324984774000,    -   ‘previous_hash’:

“2cf24dba5fb0a30e26e83b2ac5b9e29e1b161e5c1fa7425e74043362938b9824”

}

newTransaction

(sender,

recipient,

amount,

component,

product feature,

review) {

-   -   this.current_transaction.push({    -   sender: sender,    -   recipient: recipient,    -   amount: amount    -   component: component    -   product feature: feature    -   review: review    -   })    -   return this.lastBlock( )[‘index’]+1        -   }

As illustrated in FIG. 8A, in one or more embodiments, program code cantrack ownership of the product via the blockchain database frommanufacturer, to distributor, to end-user or purchaser (i.e., owner)810. For instance, as a product changes hands from the manufacturer tothe distributor and finally to the purchaser, the ownership of the finalproduct can be tracked in a data structure, such as part of theblockchain database, or separate, and can be updated to reflect, inaddition to the current ownership information, the particular blockchaindatabase record, which contains information on all the components in theproduct, as described herein.

Based on receipt of review data for the product, program code verifiesthe review data 815. For instance, the program code can authenticate avalidator providing the review data, or correlate an identifierassociated with the review data to an identifier associated with theproduct. In particular, in one embodiment, when a review is left orposted for a product, the reviewer is able to authenticate themselves asowner of the product that they are leaving the review for. Thisauthentication process advantageously prevents spammed versions beingleft by non-product owners or automated part or product reviews by bots.

Based on data analysis, program code links the verified review data tothe product's blockchain record 820. Thus, when a review is left, ratherthan being a generic review for a generic product of a certain model,the review will reference the exact product record that includes theproduct's supply chain history, which identifies (for instance) specificcomponents used within the product, as well as manufacturing processdetails. This linking enables more granular consideration of positiveand negative review data with reference to a product's supply chain, andthe correlation between the particular components of the product and thereview data.

Program code then provides to a user product-related guidance based, atleast in part, on the verified review data that has been linked to theblockchain database for the product's supply chain history 825.

In one or more embodiments, the product-related guidance can vary,depending upon the system user. For instance, where the user is aconsumer, then as illustrated in FIG. 8B, the program code can providethe verified review data to the consumer as part of the product-relatedguidance 830. In addition, program code can optionally determine for theconsumer whether another product being considered for purchase by theconsumer has the same components as recorded in the blockchain databaselinked to the product associated with the verified review data 835. Inthis manner, the consumer is provided with verified reviews for theparticular product, model, component, being considered for purchase, andhas confidence that the particular product, model, component beingconsidered is identical to the product associated with the verifiedreview data. In this regard, the program code can be provided withaccess to the blockchain record of the particular product, model,component, being considered for purchase, which is in addition to theblockchain record of the product associated with the verified reviewdata. Further, the program code can identify one or more differencesbetween the product associated with the verified review data, and theproduct being considered for purchase, and provide information to theconsumer identifying those one or more differences, such as one or moresupply chain differences or component differences in the history of thetwo products.

For a system user that is a supply chain entity, program code can usenatural language processing and associated clustering to categorize theverified review data, and predictive modeling to generate one or morerecommendations related to the product's supply chain 840, asillustrated in FIG. 8C. The generated recommendations can be provided bythe program code to the one or more supply chain entities as, at leastin part, product-related guidance 845, which can then be implemented bythe supply chain entity. For instance, in one or more embodiments, basedon the product-related guidance, one or more supply chain entities canchange a component supplier, change a component in the product, change amanufacturing process, a transport process, etc., in the product'ssupply chain.

By way of example, a Bag of Words NLP algorithm can be applied to thereview data, and an associated clustering model used to congregate thereview data to the blockchain node network, with the component orproduct identified, and a Naïve Bayes classifier can be used by theprogram code for prediction analysis of similar components existing inthe framework. For instance, applying NLP-based analysis can be appliedto product review data and product specifications to identify aparticular component used in various models of a product, even where theproducts are not necessarily the same or the same model, or can havedifferent variations of product components. Associating the verifiedreview data with variations in the model components, as well asvariations in the manufacturing pipeline, can be accomplished byassociative clustering, natural language processing, keywordsidentification, etc., which can be used to cluster different items,components, products, based on associative clustering, and then predictimpact on other similar items based on commonalities and specifications,one embodiment of which is described further below with reference toFIG. 9. By way of example only, the below program code might be used inone embodiment:

for i in range(0,1000):

-   -   review=re.sub(‘[{circumflex over ( )}a-zA-Z]’, ‘ ’,        dataset[‘Review’][i])    -   review=review.lower( )    -   review=review.split( )        -   ps=PorterStemmer( )        -   review=[ps.stem(word) for word in view if not word in            set(stopwords.words(‘english’))]    -   review=‘ ’.join(review)    -   corpus.append(review)    -   # Creating the Bag of Words model    -   from sklearn.feature_extraction.text import CountVectorizer    -   cv=CountVectorizer(max_features=1500)    -   X=cv.fit_transform(corpus).toarray( )    -   y=dataset.iloc[:, 1].values    -   # Splitting the dataset into the Training set and Test set    -   from sklearn.cross_validation import train_test_split    -   X_train, X_test, y_train, y_test=train_test_split(X, y,        test_size=0.20 random_state=0)    -   # Fitting Naïve Bayes to the Training set    -   from sklearn.naive_bayes import GaussianNB    -   classifier=GaussianGB( )    -   classifier.fit(X_train, y_train)    -   # Predicting the Test set results    -   y_pred=classifier.predict(X_test)        In one or more embodiments, recommendation engine test results        can be fed to the computing node(s) with the record data for a        particular component, with the information being presented to a        system user in a verified format.

Those skilled in the art will note from the above description that thedata-analysis-based validation of product review data and linking tosupply chain record data can be used herein to provide a variety ofproduct-related guidance, depending on the system user, and basedthereon, for a variety of actions to be taken. For instance, when aconsumer wishes to purchase an item based on one or more reviews, theconsumer, using the system/processing disclosed herein, is able toindependently verify that a product to be purchased does indeed containthe same components as the one that the review was written for,providing the consumer with a much higher level of confidence in thereview data. In another embodiment, the review data can be collected asfeedback, categorized as described herein, and a set of actionablerecommendations can be generated by the recommendation engine forpresentation to a company or manufacturer, such as a company ormanufacturer in the supply chain ecosystem, to better refine theirmanufacturing process and/or product, and to obtain a moreconsumer-friendly outcome.

FIG. 9 depicts a further embodiment of a workflow that illustratesvarious aspects of some embodiments of the present invention. In FIG. 9,respective specification data for two different products, ITEM1 902 andITEM2 903, is obtained, for instance, from the product's associatedblockchain record data 901. The products can be, for instance, differentproducts of the same model product, or different models, or differenttypes of products completely, which may use one or more commoncomponents within the products. Review data for ITEM1 904 and reviewdata for ITEM2 905 is received by the system from one or more thirdparty validators 910, such as one or more consumers of the ITEM1 productand the ITEM2 product.

In one or more implementations, the recommendation engine uses naturallanguage processing and AI clustering techniques to evaluate semanticsimilarities and compare the sematic similarities to one or morethreshold scores 920. For instance, semantic similarity between ITEM1specification 902 and ITEM2 specification 903 can be evaluated andcompared against one or more thresholds to cluster the items together,for instance, based on the items being a similar model item, or havingone or more common components, or other commonality in theirspecification. In one or more embodiments, the semantic similaritybetween the product review data 904, 905 for the different items iscompared to one or more thresholds to cluster the reviews (i.e.,feedback) for those items 922. For instance, in one embodiment, thesystem clusters the ITEM1 and ITEM2 products together based on naturallanguage processing of the associated specifications, and then clustersthe feedback for those items together as appropriate to provide data toa predictive model for, for instance, evaluating impact of productpipeline variations on the end product 930. This use of a predictivemodel 930 can also reference the associated blockchain record data 901for the products documenting, at least in part, the respective supplychain histories.

In the embodiment illustrated, the respective blockchains is updatedwith reference to the clustered feedback data, or clustered feedbackidentification 940, and product-related guidance is provided by thesystem to one or more users. For instance, where the user is a consumer,verifiable reviews for the end-user for the particular product, model,component, etc., are provided 950. Where the user is a supply chainentity, such as a manufacturer, one or more recommendations are providedto the supply chain ecosystem entity 960. Based on the verified reviewsand the granularity of the supply chain data available, the user thentakes action to, for instance, purchase a related product, decide not topurchase a related product, or make an adjustment to the supply chain ormanufacturing process to improve future reviews of the product, etc.

One or more aspects may relate to cloud computing.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based email). Theconsumer does not manage or control the underlying cloud infrastructureincluding network, servers, operating systems, storage, or evenindividual application capabilities, with the possible exception oflimited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

A cloud computing node can include a computer system/server such as theone depicted in FIG. 3. Computer system/server 302 of FIG. 3 can bepracticed and distributed in cloud computing environments where tasksare performed by remote processing devices that are linked through acommunications network. In a distributed cloud computing environment,program modules can be located in both local and remote computer systemstorage media, including memory storage devices. Computer system/server302 is capable of being implemented to perform the functionality setforth herein.

Referring now to FIG. 10, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 52 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 52 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 10 are intended to be illustrative only and that computing nodes52 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 11, a set of functional abstraction layersprovided by cloud computing environment 50 (FIG. 10) is shown. It shouldbe understood in advance that the components, layers, and functionsshown in FIG. 11 are intended to be illustrative only and embodiments ofthe invention are not limited thereto. As depicted, the following layersand corresponding functions are provided.

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and review data authentication and linkingmodule processing and/or recommendation engine processing 96.

Aspects of the present invention may be a system, a method, and/or acomputer program product at any possible technical detail level ofintegration. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a computer, or other programmable data processing apparatusto produce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks. These computerreadable program instructions may also be stored in a computer readablestorage medium that can direct a computer, a programmable dataprocessing apparatus, and/or other devices to function in a particularmanner, such that the computer readable storage medium havinginstructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be accomplished as one step, executed concurrently,substantially concurrently, in a partially or wholly temporallyoverlapping manner, or the blocks may sometimes be executed in thereverse order, depending upon the functionality involved. It is notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts or carry outcombinations of special purpose hardware and computer instructions.

In addition to the above, one or more aspects may be provided, offered,deployed, managed, serviced, etc. by a service provider who offersmanagement of customer environments. For instance, the service providercan create, maintain, support, etc. computer code and/or a computerinfrastructure that performs one or more aspects for one or morecustomers. In return, the service provider may receive payment from thecustomer under a subscription and/or fee agreement, as examples.Additionally or alternatively, the service provider may receive paymentfrom the sale of advertising content to one or more third parties.

In one aspect, an application may be deployed for performing one or moreembodiments. As one example, the deploying of an application comprisesproviding computer infrastructure operable to perform one or moreembodiments.

As a further aspect, a computing infrastructure may be deployedcomprising integrating computer readable code into a computing system,in which the code in combination with the computing system is capable ofperforming one or more embodiments.

As yet a further aspect, a process for integrating computinginfrastructure comprising integrating computer readable code into acomputer system may be provided. The computer system comprises acomputer readable medium, in which the computer medium comprises one ormore embodiments. The code in combination with the computer system iscapable of performing one or more embodiments.

Although various embodiments are described above, these are onlyexamples. For example, other types of devices and/or tracking componentsmay be used in one or more embodiments. Many variations are possible.

Further, other types of computing environments can benefit and be used.As an example, a data processing system suitable for storing and/orexecuting program code is usable that includes at least two processorscoupled directly or indirectly to memory elements through a system bus.The memory elements include, for instance, local memory employed duringactual execution of the program code, bulk storage, and cache memorywhich provide temporary storage of at least some program code in orderto reduce the number of times code must be retrieved from bulk storageduring execution.

Input/Output or I/O devices (including, but not limited to, keyboards,displays, pointing devices, DASD, tape, CDs, DVDs, thumb drives andother memory media, etc.) can be coupled to the system either directlyor through intervening I/O controllers. Network adapters may also becoupled to the system to enable the data processing system to becomecoupled to other data processing systems or remote printers or storagedevices through intervening private or public networks. Modems, cablemodems, and Ethernet cards are just a few of the available types ofnetwork adapters.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise” (andany form of comprise, such as “comprises” and “comprising”), “have” (andany form of have, such as “has” and “having”), “include” (and any formof include, such as “includes” and “including”), and “contain” (and anyform contain, such as “contains” and “containing”) are open-endedlinking verbs. As a result, a method or device that “comprises”, “has”,“includes” or “contains” one or more steps or elements possesses thoseone or more steps or elements, but is not limited to possessing onlythose one or more steps or elements. Likewise, a step of a method or anelement of a device that “comprises”, “has”, “includes” or “contains”one or more features possesses those one or more features, but is notlimited to possessing only those one or more features. Furthermore, adevice or structure that is configured in a certain way is configured inat least that way, but may also be configured in ways that are notlisted.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below, if any, areintended to include any structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description of the present invention has been presented forpurposes of illustration and description, but is not intended to beexhaustive or limited to the invention in the form disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The embodiment was chosen and described in order to best explain theprinciples of one or more aspects of the invention and the practicalapplication, and to enable others of ordinary skill in the art tounderstand one or more aspects of the invention for various embodimentswith various modifications as are suited to the particular usecontemplated.

What is claimed is:
 1. A computer-implemented method comprising:obtaining, by one or more processors, a digital blockchain recorddocumenting, at least in part, a product's supply chain history, thedigital blockchain record being maintained by a blockchain system;receiving, by the one or more processors, digital review data for theproduct; data-analysis-based authenticating, by the one or moreprocessors, the digital review data of the product to establish verifiedreview data; data-analysis-based linking, by the one or more processors,the verified review data to the digital blockchain record to produce alinked digital record documenting, at least in part, the product'ssupply chain history and the linked, verified review data; and providingto a user, by the one or more processors, product-related guidancebased, at least in part, on the linked digital record documenting, atleast in part, the product's supply chain history and the linked,verified review data.
 2. The computer-implemented method of claim 1,further comprising: receiving, by the one or more processors, otherrecord data documenting, at least in part, another product's supplychain history, where the product and the other product are a same typeof product; comparing, by the one or more processors, the digitalblockchain record of the product and the other record data of the otherproduct to confirm that the product and the other product containidentical components; and providing to the user, by the one or moreprocessors, an indication that the product and the other product containthe identical components.
 3. The computer-implemented method of claim 1,further comprising: receiving, by the one or more processors, otherrecord data documenting, at least in part, another product's supplychain history, where the product and the other product are a same typeof product; comparing, by the one or more processors, the digitalblockchain record of the product and the other record data of the otherproduct to identify one or more supply chain differences between theproduct and the other product; and providing, by the one or moreprocessors, the supply chain difference(s) to the user.
 4. Thecomputer-implemented method of claim 3, wherein the one or more supplychain differences between the product and the other product comprise oneor more component differences between the product and the other product,where the product and the other product each comprise multiplecomponents.
 5. The computer-implemented method of claim 1, wherein thedigital blockchain record comprises product component details, and themethod further comprises analyzing, by the one or more processors, theverified review data using natural language processing to identify basedthereon a component of the product relevant, at least in part, to theverified review data, and wherein the providing includes indicating tothe user the identified component of the product relevant to theverified review data.
 6. The computer-implemented method of claim 5,further comprising: receiving, by the one or more processors, otherrecord data documenting, at least in part, another product's supplychain history, where the product and the other product are differenttypes of products; comparing, by the one or more processors, the digitalblockchain record of the product and the other record data of the otherproduct to confirm that the identified component is common between theproduct and the other product; and providing to the user, by the one ormore processors, an indication that the identified component is commonbetween the product and the other product.
 7. The computer-implementedmethod of claim 1, wherein the user is a manufacturer of the product,and the verified review data is provided by an end-user of the product,and wherein the providing includes parsing, by the one or moreprocessors, the verified review data using natural language processingand generating based thereon feedback guidance to the manufacturerspecific to one or more aspects of the product's supply chain history.8. The computer-implemented method of claim 7, wherein the feedbackguidance comprises one or more machine learning recommendations to themanufacturer pertaining the product's supply chain, the one or morerecommendations being based, at least in part, on the verified reviewdata for the product.
 9. The computer-implemented method of claim 1,wherein the digital blockchain record comprises ownership data for theproduct, and the authenticating comprises comparing identity data of avalidator providing the review data to the ownership data for theproduct to authenticate the validator, and thereby verify the reviewdata.
 10. A system comprising: a memory; one or more processors incommunication with the memory; and program instructions executable bythe one or more processors via the memory to perform a methodcomprising: obtaining, by one or more processors, a digital blockchainrecord documenting, at least in part, a product's supply chain history,the digital blockchain record being maintained by a blockchain system;receiving, by the one or more processors, digital review data for theproduct; data-analysis-based authenticating, by the one or moreprocessors, the digital review data of the product to establish verifiedreview data; data-analysis-based linking, by the one or more processors,the verified review data to the digital blockchain record to produce alinked digital record documenting, at least in part, the product'ssupply chain history and the linked, verified review data; and providingto a user, by the one or more processors, product-related guidancebased, at least in part, on the linked digital record documenting, atleast in part, the product's supply chain history and the linked,verified review data.
 11. The system of claim 10, wherein the recorddata includes a blockchain record documenting, at least in part, theproduct's supply chain history, and wherein the method furthercomprises: receiving, by the one or more processors, other record datadocumenting, at least in part, another product's supply chain history,where the product and the other product are a same type of product;comparing, by the one or more processors, the digital blockchain recordof the product and the other record data of the other product to confirmthat the product and the other product contain identical components; andproviding to the user, by the one or more processors, an indication thatthe product and the other product contain the identical components. 12.The system of claim 10, wherein the method further comprises: receiving,by the one or more processors, other record data documenting, at leastin part, another product's supply chain history, where the product andthe other product are a same type of product; comparing, by the one ormore processors, the digital blockchain record of the product and theother record data of the other product to identify one or more supplychain differences between the product and the other product; andproviding, by the one or more processors, the supply chain difference(s)to the user.
 13. The system of claim 10, wherein the digital blockchainrecord comprises product component details, and wherein the methodfurther comprises analyzing, by the one or more processors, the verifiedreview data using natural language processing to identify based thereona component of the product relevant, at least in part, to the verifiedreview data, and wherein the providing includes indicating to the userthe identified component of the product relevant to the verified reviewdata.
 14. The system of claim 13, further comprising: receiving, by theone or more processors, other record data documenting, at least in part,another product's supply chain history, where the product and the otherproduct are different types of products; comparing, by the one or moreprocessors, the digital blockchain record of the product and the otherrecord data of the other product to confirm that the identifiedcomponent is common between the product and the other product; andproviding to the user, by the one or more processors, an indication thatthe identified component is common between the product and the otherproduct.
 15. The system of claim 10, wherein the user is a manufacturerof the product, and the verified review data is provided by an end-userof the product, where the providing includes parsing, by the one or moreprocessors, the verified review data using natural language processingand generating based thereon feedback guidance to the manufacturerspecific to one or more aspects of the product's supply chain history.16. The system of claim 15, wherein the feedback guidance comprises oneor more machine learning recommendations to the manufacturer pertainingthe product's supply chain, the one or more recommendations being based,at least in part, on the verified review data for the product.
 17. Acomputer program product comprising: a computer-readable storage mediumhaving computer-readable code embodied therein, the computer-readablecode being executable by one or more processors to cause the one or moreprocessors to: obtain, by one or more processors, a digital blockchainrecord documenting, at least in part, a product's supply chain history,the digital blockchain record being maintained by a blockchain system;receive, by the one or more processors, digital review data for theproduct; data-analysis-based authenticate, by the one or moreprocessors, the digital review data of the product to establish verifiedreview data; data-analysis-based link, by the one or more processors,the verified review data to the digital blockchain record to produce alinked digital record documenting, at least in part, the product'ssupply chain history and the linked, verified review data; and provideto a user, by the one or more processors, product-related guidancebased, at least in part, on the linked digital record documenting, atleast in part, the product's supply chain history and the linked,verified review data.
 18. The computer program product of claim 17,wherein the computer-readable code is executable by the one or moreprocessors to further cause the one or more processors to: receive, bythe one or more processors, other record data documenting, at least inpart, another product's supply chain history, where the product and theother product are a same type of product; compare, by the one or moreprocessors, the digital blockchain record of the product and the otherrecord data of the other product to confirm that the product and theother product contain identical components; and provide to the user, bythe one or more processors, an indication that the product and the otherproduct contain the identical components.
 19. The computer programproduct of claim 17, wherein the user is a manufacturer of the product,and the verified review data is provided by an end-user of the product,and wherein the providing includes parsing, by the one or moreprocessors, the verified review data using natural language processingand generating based thereon feedback guidance to the manufacturerspecific to one or more aspects of the product's supply chain history.